Optimal management of logistic activities in multi-site environments

The new emerging area of Enterprise Wide Optimization (EWO) has focused the attention in effectively solving the combined production/distribution scheduling problem. The importance of logistic activities performed in multi-site environments comes from the relative magnitude of the associated transportation costs and the good chance of getting large savings on such expenses. This paper first develops an exact MILP mathematical formulation for the multiple vehicle time-window-constrained pickup and delivery (MVPDPTW) problem. The approach is able to account for many-to-many transportation requests, pure pickup and delivery tasks, heterogeneous vehicles and multiple depots. Optimal solutions for a variety of benchmark problems with cluster/random distributions of pickup and delivery locations and limited sizes in terms of customer requests and vehicles have been discovered. However, the computational cost exponentially grows with the number of requests. For large-scale m-PDPTW problems, a local search improvement algorithm steadily providing a better solution through two evolutionary steps is also presented. A neighborhood structure around the starting solution is generated by first allowing multiple request exchanges among nearby trips and then permitting the reordering of nodes on every individual route. If a better set of routes is found, both steps are repeated until no improved solution is discovered. Compact MILP mathematical formulations for both sub-problems have been developed and solved through an efficient branch-and-bound algorithm. A significant number of large-scale m-PDPTW benchmark problems, some of them including up to 100 transportation requests, were successfully solved in reasonable CPU times.     

A fault detection and isolation filter for discrete linear systems

The problem of fault and/or abrupt disturbances detection and isolation for discrete linear systems is analyzed in this work. A strategy for detecting and isolating faults and/or abrupt disturbances is presented. The strategy is an extension of an already existing result in the continuous time domain to the discrete domain. The resulting detection algorithm is a Kalman filter with a special structure. The filter generates a residuals vector in such a way that each element of this vector is related with one fault or disturbance. Therefore the effects of the other faults, disturbances, and measurement noises in this element are minimized. The necessary stability and convergence conditions are briefly exposed. A numerical example is also presented.     

Heterogeneous SoC-based acceleration of MPEG-7 compliance image retrieval process

With the growing amount of multimedial content over the internet and broadcast systems, mechanisms for efficient information organization, manipulation and transmission are becoming indispensable. Optimization of the multimedia search and retrieval processes is nowadays an important area of development due to the difficulty to browse, filter and manage that big amount of data. The adoption of the MPEG-7 standard has a significant importance to simplify the image retrieval process. However, performance issues are still relevant when the retrieval must be accomplished in real time. This work presents an innovative and efficient approach of a Content-Based Retrieval Process using metric spaces implemented in heterogeneous resources according to the demand of computational power. Several implementations were made and comparative results are shown evidencing the benefits of the proposed approach.     

An approach to implement electricity metering in real-time using artificial neural networks

As many utilities move toward deregulation, the research focus on spot pricing of electricity has led to the development of complex spot pricing-based electricity rate models. As research matures to implementation stages, approaches to meter the actual power consumption in real time are required. In this work, the authors model a real-time electric power metering approach based on neural networks. A carefully designed artificial neural network (ANN) is trained to recognize the complex optimal operating point of an all-thermal electricity generating utility. A real-time rate is allocated to each bus for a given power system's loading pattern and the recall process is instantaneous. The proposed approach is tested using a spot pricing model on five- and 14-bus electric power systems. Different loading levels are used for each bus.     

Simulation results for on-line optimization of a batch bioreactor using nonlinear filtering and optimal control

The computation of optimal control profiles for batch bioreactors is based on the use of simple and empirical dynamic models. Since these models present some level of uncertainty, the difference between the model dynamics and the reactor dynamics can have significant effects in the reliability of the calculated profile. To develop near optimal control trajectories considering this drawback, we propose to calculate successive control profiles on a moving time horizon using a mathematical model in which the kinetic parameters are estimated by an observer. The desired objective is to generate a near optimal control trajectory adapted to the "running" fermentation. This idea results in a nonlinear estimator plus an optimizer arrangement that so far has not been applied to batch fermentors. Numerical simulations are performed on xanthan-gum batch fermentations and reasonably good results are obtained.     

A method for detection and diagnosis on batch fermentations

In this work we present some basic ideas about detection and diagnosis of faults and abrupt dynamic changes in batch fermentations. Our work focuses on the simultaneous use of two detection methods (residual based and balances based) within the estimation procedure. The idea behind the use of both methods is that the weakness of one of them can be compensated by the use of the other one. Thus the simultaneous use of both methods allows detecting and possibly isolating a wide range of faults. Observations such as the effect of nonlinearities on the detection tests and robustness to model uncertainty are discussed. Numerical results on a particular case, the xanthan gum batch fermentation, are presented. Simulated faults and abnormal behaviors were promptly detected but diagnostics showed mixed results.     

A reactive MILP approach to the multidepot heterogeneous fleet vehicle routing problem with time windows

The time‐window‐constrained vehicle routing problem (VRPTW) is a well‐known combinatorial problem. Its goal is to discover the best set of routes for a vehicle fleet in order to service a given number of customers at minimum cost. Vehicle capacity, maximum service time and time‐window constraints must be satisfied. Most proposed VRPTW optimizing approaches intend to discover the best or a near‐optimal solution at once. Improvement methods are old strategies that apply heuristics to insert customers into tours and/or rearrange nodes to obtain better routes. They are performed until no further improvement is achieved. Little research has been focused on model‐based reactive approaches seeking a better solution by exploring a small solution space around the current solution. This work presents a new model‐based improvement methodology for the multi‐depot heterogeneous‐fleet VRPTW problem to enhance an initial solution through solving a series of MILP mathematical problems that allow exchanges of nodes among tours and node reordering on every route. By restricting the range of improvement options, the problem size can be bounded and a limited number of binary variables is required for real‐world problems. The improvement formulation is based on a continuous time‐domain representation that handles assignment and sequencing decisions through different sets of binary variables and uses the notion of a generalized predecessor instead of a direct predecessor. Several types of VRPTW problems have been efficiently solved.